Inspired by CoAD [1]}, in the training phase, in addition to the group inputs \(\mathcal {I}\)  loaded from a CoSOD dataset, we simultaneously feed \(K\)  auxiliary samples loaded from a SOD dataset into the shared CNN backbone and TSIR, generating single-image saliency maps \(\mathcal {H}=\lbrace H^{k)}\rbrace _{k=1}^K\) . The saliency and co-saliency prediction is jointly optimized as a multi-task learning
framework with better flexibility and expansibility in terms of providing reliable saliency priors.
